Beyond green labels: exploring comfort perception in educational spaces through immersive virtual reality

Educational buildings sit at the intersection of sustainability, user experience, and environmental performance, where design decisions directly shape learning and engagement. Although sustainable frameworks, green certifications, and automated control systems aim to enhance indoor environmental quality (IEQ) (Feige et al., 2013; Pastore and Andersen, 2019; Sant’Anna et al., 2018), post-occupancy studies continue to report comfort challenges even in high-performing buildings (Lai et al., 2009; Pastore and Andersen, 2019). Certification systems primarily evaluate measurable environmental indicators, yet they often provide limited insight into how occupants actually experience comfort in specific spaces. IEQ encompasses thermal, visual, acoustic, and air quality conditions. Among these, thermal comfort (TC), visual comfort (VC), and acoustic comfort (AC) are repeatedly shown to influence students' well-being and productivity (Beckers et al., 2016; Clausen and Wyon, 2008; Frontczak and Wargocki, 2011; Jiang et al., 2022; Korsavi et al., 2020; Riaz et al., 2025; Wu et al., 2023), forming the core dimensions examined in this study. User-centered investigations of learning environments likewise show that educational spaces should be assessed in relation to how teaching and learning activities are actually experienced by occupants rather than through technical standards alone (Salama, 2009).

The Hue–Heat Hypothesis (HHH) suggests that visual cues, including color and light temperature, shape thermal sensation and comfort perception (Cui and Iwaki, 2023; Huebner et al., 2016; Kim et al., 2023; Kocur et al., 2023; Picco and Bernagozzi, 2024; Pigliautile et al., 2023; Ziat et al., 2016). In immersive environments, these cross-modal associations shape subjective and physiological responses (Kocur et al., 2023; Li et al., 2021; Wu et al., 2023). This also links to adaptive comfort perspectives, where comfort is negotiated through both environmental stimuli and adaptive interpretation rather than driven by thermal parameters alone (Frontczak and Wargocki, 2011; Huebner et al., 2016). In operational educational spaces, visual comfort therefore functions as more than visibility, as it can shape comfort-related interpretation and influence how readily users tolerate or adapt to thermal conditions (Kim et al., 2023; Wu et al., 2023). Yet, many VR comfort studies use passive observation designs (e.g. Kuliga et al., 2015; Wu et al., 2023), while others collect comfort ratings during immersion under structured experimental conditions (e.g. Kocur et al., 2023; Wu et al., 2023; Yeom et al., 2019). Such approaches may direct attention to comfort more explicitly than studies that examine how it emerges during more demanding task-based engagement. Discomfort during learning can reduce attentional capacity and hinder academic outcomes (Beckers et al., 2016; Korsavi et al., 2020; Sharma et al., 2025). Because post-construction interventions are costly and disruptive, adaptable methods for exploring perceived comfort in operational buildings are needed.

Post-occupancy evaluations also highlight tension between sustainability measures and occupant experience. Automated HVAC, shading, lighting, or ventilation systems can restrict personal control, contributing to dissatisfaction despite efficient performance (Pastore and Andersen, 2019; Raja et al., 2001). In this context, VR-based comfort cues and perceived control strategies may offer a complementary pathway in a simulated post-occupancy context to support user experience without increasing energy demand or requiring changes to building systems. Highly automated, energy-oriented strategies can reduce environmental adaptability by limiting opportunities for occupant control (Feige et al., 2013; Raja et al., 2001). Across building types, user control strongly predicts comfort and satisfaction, while limited opportunities for control often undermine these outcomes (Pastore and Andersen, 2019). In educational environments, where occupants spend long hours and have diverse needs, flexibility becomes especially important (Mansor et al., 2025; Zierke et al., 2023). Recent user-centered assessment research in university environments also emphasizes the value of technology-enabled methods for capturing richer feedback on how educational settings are experienced and adapted to users' evolving needs (Patil et al., 2024).

Virtual Reality (VR) offers opportunities to simulate indoor environments and test environmental adjustments before physical implementation (Heydarian et al., 2015; Vittori et al., 2021; Yeom et al., 2019). Prior research uses VR primarily for pre-occupancy validation and design-phase evaluation (Heydarian et al., 2015; Noghabaei et al., 2020). Typical applications include evaluation of lighting, layout, and biophilic elements (Birt et al., 2017; Kuliga et al., 2015; Latini et al., 2024; Niu et al., 2016). However, studies addressing perceived comfort in already occupied buildings remain limited. Many rely on preset scenarios and passive exposure (Hiyasat et al., 2025; Wu et al., 2023), or collect comfort ratings in VR during structured exposure (Kocur et al., 2023; Wu et al., 2023), while offering minimal real-time user control. Task-based VR studies exist (Günther et al., 2020; Kocur et al., 2023), although studies incorporating user-selectable, ecologically relevant comfort adjustments remain limited. Overall, the literature shows limited integration of dynamic environmental control and task engagement in VR studies situated within occupied-building contexts.

Gaps persist regarding multisensory interactions and perceived control. Within VR cross-modal comfort research, studies have largely focused on visual–thermal interactions, while acoustic interactions are less frequently examined in this context (Lai et al., 2009; Wu et al., 2023). Real-time control, the ability to influence environmental conditions during immersion, is rarely included. Accordingly, many systems reported in the literature rely on comparisons across discrete, researcher-defined scenarios or fixed condition sets (e.g. Chinazzo et al., 2021; Erickson et al., 2019; Günther et al., 2024; Hiyasat et al., 2025; Kim et al., 2023; Kocur et al., 2023; Kuliga et al., 2015; Latini et al., 2024; Li et al., 2021), whereas task-based environmental control is less commonly implemented; one example is Niu et al. (2016), where participants completed a task while actively controlling lighting within the virtual setting. Emerging work combines VR with sensor-based monitoring and, in some cases, physiological measures to support indoor-environment evaluation and optimization (Hu, 2025; Li et al., 2021), but immersive approaches that combine environmental control, cognitive engagement, and subjective evaluation remain scarce, particularly in educational settings where adaptability supports concentration.

Comfort theories help explain these gaps. The adaptive comfort model frames comfort as a negotiated state shaped by environmental stimuli and an individual's adaptive capacity (Frontczak and Wargocki, 2011; Huebner et al., 2016; Raja et al., 2001; Wu et al., 2023). Studies consistently show that user control enhances perceived comfort and satisfaction (Feige et al., 2013; Frontczak and Wargocki, 2011; Pastore and Andersen, 2019; Raja et al., 2001). Environmental psychology further highlights how personal control beliefs support comfort and emotional well-being (Mansor et al., 2025). In educational settings, comfort is closely tied to attentional capacity and learning performance, and discomfort can disrupt students' engagement and outcomes (Beckers et al., 2016; Korsavi et al., 2020; Sharma et al., 2025). Across building types, adaptive comfort and post-occupancy research also emphasize that opportunities for user control shape comfort and satisfaction, which is particularly relevant in operational learning environments where flexibility supports sustained concentration (Pastore and Andersen, 2019; Raja et al., 2001). Expectation effects demonstrate that visual cues can influence interpretations of thermal and lighting conditions even without physical change (Kim et al., 2023; Picco and Bernagozzi, 2024; Pigliautile et al., 2023; Terblanche and Khumalo, 2025; Ziat et al., 2016). These theories suggest that comfort emerges from an interplay of perception, control, and interpretation.

Building on these foundations, the present study introduces a placebo-like manipulation to examine how perceived control shapes comfort when environmental changes occur subtly or go unnoticed. Here, “placebo-like” refers to a condition in which participants believe they retain control or that a selected adjustment remains active. Meanwhile, the underlying setting is gradually reverted without explicit notice, allowing assessment of whether perceived control sustains comfort perception despite reduced actual environmental change. The study adopts an exploratory, task-based VR methodology that examines comfort perception during immersive interaction rather than relying on traditional survey-based post-occupancy evaluations. This aligns with cross-modal perception frameworks, which propose that sensory cues can shape comfort-related interpretations even when the underlying thermal conditions are held constant or only minimally changed (Kim et al., 2023; Picco and Bernagozzi, 2024; Pigliautile et al., 2023).

To address the identified gaps, this exploratory study examines perceived comfort in an operational classroom at KU Leuven using a high-fidelity VR model of an already occupied classroom. This enables participants to experience and reflect on comfort conditions within a simulated occupied-building context. Participants completed a meaningful design-related task while adjusting selected visual and visually simulated thermal cues. A gradual lighting fade-back tested comfort responses when perceived control diverged from actual environmental change. Figure 1 presents the theoretical framework, highlighting perceived control, environmental adjustability, multisensory comfort, and awareness of change within immersive environments.

The study is guided by three exploratory research questions:

By examining comfort during task-based interaction in a simulated occupied-building context and comparing perceived and actual control, this study offers preliminary insights to support user-centered evaluation strategies for operational educational settings.

This section describes the study design, virtual environment, experimental conditions, participants, procedures, measures, and analytical approach. The study adopts an exploratory, task-based VR methodology designed to investigate how comfort perception develops during immersive interaction within a simulated occupied-building context.

This exploratory study examined how participants experienced comfort while completing a design-related task across four staged virtual environments. The experiment followed a fixed sequence of conditions that progressed from a neutral baseline to environments that allowed participants to select physical-control and lighting-control options, and ended with a placebo-like condition in which lighting underwent a gradual fade-back to baseline. A fixed order was used to ensure that all participants experienced the same progression of environmental adjustments, allowing consistent observation of how perceived comfort evolved during task-based immersion across the staged conditions.

Sessions were conducted in indoor spaces at KU Leuven (Ghent campus), and Zayed University (Abu Dhabi campus). The virtual environment used in the study was based on an operational classroom at KU Leuven, which served as the physical reference for the VR reconstruction. The room's geometry, furniture layout, and material finishes were documented and replicated in the virtual model to ensure spatial consistency between the reference space and its digital counterpart. Because the study focused on comfort responses generated within the virtual environment, the physical rooms were not experimentally modified and remained in their normal teaching conditions during the sessions. The physical setting was treated as a naturally occurring background condition and was not continuously monitored (e.g. temperature, illuminance, or sound pressure level), as the experimental conditions occurred within the VR environment. Each participant completed the experiment individually during scheduled appointments. The two-site setup supported recruitment while keeping the VR model, experimental sequence, and procedure constant across sessions; it was not intended to support institutional, climatic, or cultural comparison.

The virtual environment was developed in Unity and deployed on a Meta Quest 3 headset. According to manufacturer specifications, Meta Quest 3 provides a display resolution of 2064 × 2,208 pixels per eye, refresh-rate options up to 120 Hz, and a field of view of 110° (horizontal) and 96° (vertical) (Meta, 2026). The model reconstructed a teaching studio at KU Leuven, which served as the reference space for the study. The room's geometry, furniture layout, and material finishes were documented and replicated to match the spatial characteristics and proportions of the real classroom. High-fidelity textures and consistent spatial scaling supported immersion.

The model incorporated several adjustable elements that were activated depending on the experimental condition. These included closing the curtains, activating the fireplace screen, turning on the ceiling fan, adding greenery, and selecting artificial lighting options. A continuous water sound was present across all environments to provide a stable acoustic backdrop, as natural water soundscapes have been linked with stress-reduction and improved acoustic comfort (Zhang et al., 2025). The sound was delivered through the headset's integrated speakers. Volume was set to a consistent level across participants and sessions to ensure the sound functioned as a standardized background cue rather than an experimental variable. Accordingly, acoustic comfort was treated as a stable background component of the multisensory setting rather than as an independently manipulated condition. At the start of each session, all adjustable cues were turned off, the curtains were open, and the lighting was set to a neutral level to establish the baseline condition shown in Figure 2.

The experiment consisted of four sequential virtual environments, labeled Environment 1 through Environment 4 (E1–E4), designed to examine how participants experienced comfort under varying conditions of environmental control. Each environment introduced a distinct set of visual adjustments while the core spatial layout remained constant.

E1 established a neutral reference condition based on comfort-related principles identified in the literature. The virtual classroom was presented with the curtains open to allow natural light, the lighting was set to a neutral level, and all adjustable visual elements were turned off. The continuous water sound described earlier was also present to maintain a stable acoustic backdrop. This environment served as the point of comparison for subsequent stages.

E2 introduced a physical-control condition in which participants could select one of the available options within the virtual environment. A control interface displayed these options, namely closing the curtains, turning on the fireplace screen, activating the ceiling fan, or adding greenery, and participants could preview them before confirming their selection. After confirmation, the virtual classroom updated immediately. Activating the fan or the fireplace screen added their corresponding acoustic cues, while choosing the curtains or greenery introduced only visual changes.

E3 introduced a lighting-control condition in which participants could select between warm and cool lighting options. A control interface allowed participants to preview both options before confirming their selection. The selected lighting condition remained active throughout this stage.

E4 introduced a placebo-like condition in which the lighting gradually faded back to the neutral baseline without notifying participants. Lighting was selected for the gradual fade-back because it could be altered subtly over time without interrupting immersion, making it suitable for examining whether perceived comfort could persist when perceived control no longer matched the underlying environmental state. The physical-control option selected in E2 remained visible while the lighting faded back to baseline. This stage examined how participants responded when perceived control was maintained while the underlying environmental state no longer fully matched their earlier selections.

A total of 21 participants took part in the study, with 10 recruited at KU Leuven and 11 at Zayed University. All participants were over 18 years of age and affiliated with their respective institutions as students. Recruitment was conducted through campus email, posters, and classroom announcements. Eligibility required current academic affiliation and signed informed consent, and no exclusion criteria were applied. Given the modest sample size, the student-based sample, and the involvement of two institutional contexts, the findings should be interpreted as exploratory and context-specific, emphasizing perceived comfort patterns rather than cross-site climatic or cultural comparisons, and may not generalize to broader occupant populations.

All participants completed the full VR session and the post-experiment interview. Demographic information, including gender, academic level, and prior VR experience, was collected. Prior familiarity with the operational classroom was also recorded and considered during interpretation; however, it was not treated as a formal analytic grouping factor given the qualitative emphasis and sample size. Most participants reported limited or no prior experience with VR. Participants were advised to use contact lenses when possible; two wore eyeglasses and reported no discomfort while using the headset. No participants withdrew from the study.

Each participant completed an individual session in an indoor space at KU Leuven or Zayed University. After reviewing the study information and providing informed consent, participants were introduced to the Meta Quest 3 headset and given a brief orientation on basic controls. They were informed that they could stop the session at any time. A short familiarization period followed, during which participants explored the virtual environment until they felt comfortable, typically within one to five minutes.

Participants were told that the study examined their experience while performing a design-related task in VR. To avoid priming effects, comfort was not mentioned during the session, and all comfort-related questions were deferred to the post-experiment interview.

The VR session began in E1. Participants initiated the geometric object arrangement task by selecting a virtual start button. They were asked to recreate simple arrangements of 3D shapes on a virtual table based on a reference matrix displayed in the environment (shown in Figure 3). Participants were instructed to complete the task at least once in each environment. The task was kept deliberately simple to minimize learning effects across the four sequential environments and to support the study's primary focus on perceived control rather than task-performance variation.

After approximately one minute in E1, an interface panel appeared prompting participants to select one physical-control option, initiating the physical-control condition in E2. The environment updated immediately following their selection, and participants continued the task within the physical-control condition. After another minute, a second interface panel appeared, introducing the lighting-control condition in Environment 3 and allowing participants to preview and select either warm or cool lighting before resuming the task.

In E4, a placebo-like condition was introduced in which the lighting gradually faded back to the neutral baseline without notification. The physical-control option selected in E2 remained visible throughout this transition. Participants completed the task once more under these conditions. After completing all four environments, the headset was removed and participants took part in a semi-structured interview about their experience across the sequence.

Data were collected through task-based observations, participants' choices within each environment, and a post-experiment semi-structured interview. The study prioritizes perceived comfort as reported by participants because subjective experience is a central component of comfort research and adaptive comfort theory (Frontczak and Wargocki, 2011; Huebner et al., 2016). During the VR session, the researcher recorded which physical-control or lighting-control options participants selected while observing the session in real time. After completing all four environments, participants took part in an interview that explored their experience in each stage, including perceived comfort, reactions to the available options, whether they noticed the gradual lighting fade-back in the placebo-like condition, and reflections on the task. These interview responses constituted the primary qualitative dataset. The observed choices, awareness of the lighting fade-back, and interview reflections formed the basis for examining comfort across the four environments. Task completion time was recorded during the sessions but was not analyzed in this paper, as the present study focused on perceived comfort and perceived control rather than task-performance outcomes.

The study used a mixed qualitative–quantitative analytical approach. Interview transcripts were imported into NVivo and coded using a structured codebook. Coding categories were not treated as mutually exclusive; a single response could be assigned multiple codes when applicable. For the participant-level matrices reported (e.g. comfort dimensions × environments), a category was recorded for a participant in a given environment if it was mentioned at least once; therefore, totals across categories may exceed the sample size. Coding captured references to visual, thermal, acoustic, spatial, emotional, and cognitive comfort, as well as reflections on perceived control and awareness of environmental change. Three matrices were generated to examine patterns across environments, and these matrices informed the analytic memos that guided the interpretation of comfort responses. This structure supported comparison of how comfort-related responses shifted from baseline exposure to the physical-control condition, the lighting-control condition, and the placebo-like condition. All transcripts were coded by the first author using the predefined codebook and iterative memoing to maintain consistency. Coding was reviewed in three rounds to reduce oversight and ensure consistent application of the codebook across transcripts. As this exploratory qualitative analysis used a single-coder approach, disagreements were not applicable; ambiguous segments were revisited during the analytic memo process and coding decisions were documented.

Observed choices recorded during the VR session and participants' self-reported awareness of the lighting fade-back were summarized descriptively. Quantitative analysis was conducted in IBM SPSS Statistics (Version 30), where frequencies were calculated for key variables such as selected environmental options, comfort improvement after each option, lighting preferences, and perceived comfort across environments. Exploratory chi-square tests were used to examine associations among these variables for descriptive/illustrative purposes. Because of the modest sample size and low expected cell counts in several tables, statistical results were interpreted descriptively and used only to contextualize the qualitative patterns.

The results integrate qualitative interview data with descriptive quantitative patterns across four VR environments designed to examine how baseline perception, perceived control, and the withdrawal of control shaped comfort in a simulated occupied-building context. Across the sequence, perceived comfort was limited in the baseline condition, increased most clearly when participants could modify the environment, remained positive when lighting-control options were introduced, and remained largely stable during the fade-back. These patterns suggest that perceived control played a stronger role in shaping perceived comfort than the continued presence of specific environmental adjustments. The findings for each environment are presented in sequence.

E1 served as the reference condition and presented a neutral version of the virtual classroom. In this baseline condition, participant responses were expressed mainly through initial visual, emotional, and spatial impressions rather than through stronger comfort-related evaluations. Visual comfort was mentioned by 5 of 21 participants (23.8%), followed by emotional comfort by 4 of 21 (19.0%) and spatial comfort by 3 of 21 (14.3%), while thermal, acoustic, and cognitive comfort were not identified in this condition. These early impressions were similar regardless of whether participants had previously seen the real space.

Descriptive quantitative patterns aligned with this interpretation. Only 2 of 21 participants (9.5%) selected the baseline as the most comfortable environment, and no comfort improvements were reported because no adjustments were possible in this condition. The baseline therefore functioned as a stable comparison point. Figure 4 visually summarizes how the distribution of comfort dimensions shifted across E1–E4.

E2 introduced physical-control options such as activating the fan or fireplace, closing the curtains, or adding greenery. This was the first condition in which participants could actively modify the virtual space, and responses shifted accordingly. Emotional comfort was mentioned by 10 of 21 participants (47.6%), followed by visual comfort by 9 of 21 (42.9%), spatial comfort and acoustic comfort by 5 of 21 each (23.8%), thermal comfort by 4 of 21 (19.0%), and cognitive comfort by 3 of 21 (14.3%). Participants often linked these responses to perceived control and personalization. As one participant explained, “Because I had the choice, it felt personalized, and when you personalize a place, you are more likely to stay and enjoy it” (P13).

This qualitative emphasis on choice translated directly into the quantitative outcomes. Comfort improved for 20 of 21 participants (95.2%) after making adjustments, and 15 of 21 participants (71.4%) selected E2 as the most comfortable environment (Table 1). These results suggest that physical-control options generated the strongest comfort responses across the four conditions.

E3 introduced lighting-control options through warm and cool lighting choices, prompting predominantly visual comfort responses. Visual comfort was mentioned by 17 of 21 participants (81.0%), which was the highest visual count across all four conditions. Emotional comfort was mentioned by 8 of 21 participants (38.1%), and cognitive comfort by 5 of 21 (23.8%), suggesting that responses to lighting involved both affective and reflective evaluations. Thermal comfort was mentioned by 2 of 21 participants (9.5%), while acoustic comfort and spatial comfort were each mentioned by 1 of 21 (4.8%). Warm lighting was described as calming, while cool lighting was associated with clarity and focus, with one participant noting, “I think cold light could can make me more concentrated” (P06).

This high degree of qualitative satisfaction was clearly mirrored in the descriptive statistics. All 7 of 7 participants (100%) who selected warm lighting reported improved comfort, as did 13 of 14 participants (92.9%) who selected cool lighting (Table 2). Despite these improvements, relatively few participants selected E3 as the most comfortable environment, suggesting that lighting-control options influenced atmosphere but were less impactful than the physical-control options introduced in E2.

E4 gradually returned the lighting to baseline while maintaining the earlier physical adjustments. This fade-back created a placebo-like condition in which the environment changed without participants being informed. Comfort-related responses in this condition were limited, with emotional comfort mentioned by 4 of 21 participants (19.0%), cognitive comfort by 2 of 21 (9.5%), and visual comfort by 1 of 21 (4.8%). Participants often reported not noticing the fade-back, as one stated, “No. I wasn't paying attention to it” (P07). Even among those who noticed the change, the perceived effect was minimal, with one participant explaining, “I was actually focused on what I was doing” (P16).

The descriptive statistics confirmed this widespread lack of awareness. Seventeen of 21 participants (81.0%) did not notice the fade-back, and 18 of 21 participants (85.7%) reported that it had no effect on comfort (Table 3). No participants selected E4 as the most comfortable environment. These findings suggest that earlier opportunities to exercise perceived control had a stronger influence on comfort than the continued presence or gradual withdrawal of specific environmental changes.

The cross-cutting themes synthesize patterns that persisted across the four conditions and clarify how participants experienced comfort beyond the findings associated with each individual environment. These themes are derived from the combined qualitative and quantitative results, with Figure 5 providing a visual summary of the overall pattern.

Across the four conditions, participants consistently associated comfort with perceived control over the environment. Having options appeared to foster ease, personalization, and psychological ownership, as reflected in one participant's statement: “When I had the most choices … that was the best environment for me” (P13). These qualitative patterns aligned with the descriptive quantitative findings, with comfort improving after participant choice in both E2 and E3, as shown in Figure 5. These findings suggest that perceived control was a key driver of comfort, regardless of whether participants selected physical-control options or lighting-control options.

Many participants described lighting as an important factor shaping comfort. For example, one participant stated, “Lighting … yeah. Lighting is the most important” (P07), while another emphasized the value of natural light. However, the descriptive quantitative pattern pointed more strongly toward experienced comfort during task performance, with E2 selected far more often than E3 as the most comfortable condition (Figure 5). This contrast suggests a distinction between stated comfort preferences and embodied comfort responses during immersive task performance.

A smaller subgroup showed closer alignment between stated and embodied preferences. As one participant noted, “I tend to gravitate to the warm lighting because it just feels more comforting for me” (P01). Overall, while lighting shaped the atmosphere and visual tone of the space, embodied comfort during the task appeared to be more strongly influenced by the physical-control options available in E2.

The placebo-like condition showed that awareness of the environmental change played a limited role in shaping perceived comfort. Most participants did not notice the fade-back in E4 and often explained that their attention was directed toward the task. This pattern was also reflected in the descriptive quantitative findings, with only a small minority noticing the fade-back or reporting an effect on comfort (Figure 5).

Among the few participants who became aware of the change, the shift appeared to influence interpretation more than comfort. One participant stated, “Absolutely not … now it makes me feel like the VR was more detailed than I thought” (P18). These findings suggest that unnoticed environmental changes did not meaningfully shape perceived comfort and that earlier opportunities to exercise perceived control had a stronger influence on the overall experience than participants' moment-to-moment awareness of environmental variation.

The baseline condition produced limited comfort responses and served as a reference point for interpreting the later stages. Comfort increased most clearly in E2 and also improved in E3, although lighting appeared to play a secondary role in overall condition selection. A smaller subgroup selected E3 as the most comfortable condition, indicating that lighting played a primary role for some participants. In E4, the fade-back had little influence on perceived comfort, suggesting that perceived control in the earlier conditions shaped the overall experience more strongly than the continued presence or gradual withdrawal of environmental changes. These themes highlight the roles of perceived control, experienced comfort during task performance, and awareness in shaping perceived comfort within the simulated occupied-building context.

Exploratory cross-tabulation analyses were also conducted in SPSS to examine whether selected response patterns were associated. Because of the small sample size and low expected counts in several cells, these analyses were treated as illustrative rather than robust inferential tests. Fisher's exact tests showed no significant association between lighting-control choice in E3 and reported comfort improvement after participant choice (p = 1.000), and no significant association between noticing the fade-back and reporting that it affected comfort in E4 (p = 0.489). One exploratory association was observed between participants' general lighting preference and the lighting option selected in E3, chi-square = 9.56, df = 2, p = 0.008, Cramer's V = 0.675, suggesting that participants tended to choose a lighting condition aligned with their stated preference. Other cross-tabulations were not interpreted as robust because many cells had low expected counts.

This exploratory study examined how participants experienced environmental comfort while completing a design-related task in a simulated occupied-building context using a VR-based replica of a real classroom. By sequencing environments that differed in opportunities for environmental adjustment, the study explored how comfort developed across passive observation, active engagement, and fade-back. This approach is especially relevant in educational settings where comfort is shaped not only by environmental conditions but also by how much control users can exercise while performing demanding tasks. The study progressed through four sequential environments, ranging from a literature-informed baseline to physical-control and lighting-control adjustments and, finally, a placebo-like condition involving fade-back. The findings also suggest that symbolic VR-based cues may help support comfort in educational settings where physical control is limited. The following sections interpret these patterns in relation to existing research and discuss their methodological implications, practical relevance, limitations, and directions for future research.

Participants' comfort responses changed as different forms of control were introduced. In the baseline environment (E1), comfort was shaped mainly by visual and emotional impressions, reflecting reliance on immediate perceptual cues during passive observation. Once participants were able to make adjustments, comfort broadened and became more closely tied to active engagement with the space, particularly in the physical-control condition, where comfort extended beyond immediate visual impressions. By contrast, the lighting-control condition had a more selective influence, while the fade-back in E4 elicited little response because most participants did not notice the change.

The sequence suggests that participants' comfort became more firmly established once they were able to exercise control. The physical-control condition in E2 produced the strongest comfort responses, and although lighting influenced comfort in E3, it did not surpass the earlier physical adjustments. The unperceived lighting reversion in E4 caused little change in comfort, indicating that prior opportunities to influence the environment may have helped anchor participants' perceived comfort. These patterns highlight how different environmental cues, and the ability to manipulate them, shaped comfort during task-based immersion.

Across the conditions that involved participant adjustment, perceived control emerged as a consistent factor. Almost all participants reported increased comfort after making adjustments in both the physical-control condition and the lighting-control condition, even though the specific choices varied. This suggests that the act of modifying the environment may itself have contributed to a sense of involvement reflected in participants' comfort. The fade-back in the placebo-like condition is consistent with this interpretation, since comfort remained stable despite the unnoticed lighting shift. While this pattern is consistent with a placebo-like mechanism, alternative explanations such as expectation effects, attentional absorption during task engagement, or participants' expectations about the intended effect of the changes may also have contributed to their interpretations. These patterns offer insight into how perceived control and awareness interacted with comfort during the immersive task. The findings suggest that comfort was shaped not only by environmental cues but also by how users engaged with them across the staged sequence within the simulated occupied-building context. This provides a foundation for interpreting the findings through existing theories of comfort, perception, and environmental control.

The observed patterns align with studies describing comfort as a multidimensional and context-dependent experience (Frontczak and Wargocki, 2011; Jiang et al., 2022; Korsavi et al., 2020; Wu et al., 2023). Participants' reliance on visual and emotional impressions in the baseline environment is consistent with research showing that these domains can shape environmental evaluation, particularly through visual perception and psychological response (Jiang et al., 2022; Wu et al., 2023). When control became available, comfort expanded beyond immediate sensory impressions to include more interpretive forms of evaluation, which aligns with studies showing that occupants respond differently to environments when they are able to adapt conditions or exercise personal influence (Frontczak and Wargocki, 2011; Huebner et al., 2016; Raja et al., 2001). Maftei and Harty (2021) similarly show that immersive environmental evaluation can extend beyond simple visual preference and involve broader perceptual and interpretive responses. This also aligns with user-centered approaches to learning-environment assessment, which emphasize that educational settings should be evaluated through occupants' lived responses rather than through prescriptive measures alone (Salama, 2009).

Symbolic cues such as greenery, perceived airflow, and fireplace visuals likely contributed to participants' feelings of warmth or calm, aligning with research showing that visual indicators can influence perceived thermal comfort, and in some cases lighting-related comfort, even without physical environmental change (Kim et al., 2023; Picco and Bernagozzi, 2024; Pigliautile et al., 2023; Ziat et al., 2016). These links suggest that VR-based cues may support perceived comfort by shaping perceptual interpretations that extend beyond the physical conditions of the space.

The reported comfort improvement after participants exercised control corresponds with research emphasizing the role of user agency and personal control in shaping comfort (Mansor et al., 2025; Zierke et al., 2023). Adaptive comfort theory similarly highlights the role of personal influence over environmental conditions (Frontczak and Wargocki, 2011; Huebner et al., 2016; Raja et al., 2001). The minimal response to the E4 fade-back is also consistent with perceptual studies showing that comfort judgments can be shaped by expectation and visual bias, particularly when physical conditions remain stable or changes are subtle (Picco and Bernagozzi, 2024; Pigliautile et al., 2023; Ziat et al., 2016). These theoretical connections reinforce the interpretation that perceived control played a central role in shaping comfort perception.

This study extends VR comfort research by showing how responses developed across a staged sequence of baseline exposure, participant-selected adjustments, and a subsequent placebo-like condition within a simulated occupied-building context. Earlier VR studies have often focused on pre-occupancy design evaluation (e.g. Heydarian et al., 2015; Kuliga et al., 2015; Latini et al., 2024; Niu et al., 2016; Noghabaei et al., 2020). Many have also relied on discrete comparisons between fixed conditions (e.g. Heydarian et al., 2015; Kuliga et al., 2015; Latini et al., 2024; Niu et al., 2016; Noghabaei et al., 2020; Yeom et al., 2019), although some studies included more adjustable or task-based elements (e.g. Birt et al., 2017; Niu et al., 2016). In contrast, the present study highlights perceived control as a central interpretive mechanism during task-based immersion, while the limited impact of the placebo-like condition suggests that comfort may persist even when a selected cue fades back toward baseline without strong participant awareness. These differences position the study as an exploratory step toward understanding how perceived control and symbolic environmental cues may support perceived comfort without requiring physical changes to the operational classroom.

This study contributes three main insights. First, the staged sequence shows how comfort developed across passive observation, active adjustment, and a subsequent placebo-like condition within a single immersive experience. In this sequence, comfort was shaped not only by environmental cues but also by how participants were able to engage with and adjust them. Second, the limited influence of the fade-back suggests that perceived control may have mattered more than the continued presence of a selected adjustment, although this interpretation remains exploratory. Third, the combined findings indicate that stated comfort importance and experienced comfort during immersive task performance did not always align, particularly in a simulated occupied-building context where participants responded to symbolic and perceptual cues rather than direct physical change.

The study also advances VR-based comfort evaluation by introducing task-based immersion and staged physical-control and lighting-control conditions, expanding methodological options beyond passive exposure. The fade-back condition offers a useful conceptual lens for examining how comfort may persist when environmental cues change without explicit awareness, opening new directions for research on perceptual expectations and comfort maintenance. More broadly, the findings suggest that VR may function as a supplementary comfort resource in sustainable or shared educational spaces where physical adjustments are restricted. These contributions also highlight the value of VR as a methodological setting for examining perceptual and symbolic aspects of comfort that are difficult to isolate in real occupied spaces. Because VR allows environmental cues to be adjusted independently of physical constraints, it offers a controlled yet immersive context for studying user interpretation, perceived control, and multisensory expectation. This level of control is difficult to achieve in an operational classroom, making VR a useful platform for exploring how comfort forms and stabilizes under different conditions of environmental agency. While exploratory, this perspective reframes VR not only as a design-evaluation tool but also as a research setting for examining how comfort may be interpreted and potentially supported in simulated occupied-building contexts.

The findings have several implications for the design and operation of educational spaces. Comfort improvements following interaction with symbolic VR cues suggest that comparable visual or auditory adjustments may have potential to support perceived comfort in real classrooms when physical modifications are limited by sustainability requirements or operational constraints. These cues may complement existing systems by offering a low-energy means of supporting perceived comfort and reducing reliance on personal interventions that may conflict with energy-efficient operations. In operational classrooms where centralized systems limit individual adjustment, simulated comfort cues may offer a supplementary way to support perceived comfort during demanding tasks. This broader practical relevance is consistent with recent user-centered assessment research in university environments, which shows that technology-enabled methods can support more responsive evaluation by capturing richer feedback on how educational settings are experienced over time (Patil et al., 2024). The findings also suggest that the initial opportunity to exercise control may matter more than the continuous presence of a selected cue, since the fade-back in the placebo-like condition had limited influence on reported comfort. Considered cautiously, this suggests the potential value of VR-based comfort scenarios as supplemental rather than substitutive tools in shared learning environments. These implications are presented as exploratory and illustrative, as the study did not test real-world implementation and the findings reflect perceived comfort within a VR-based task context.

Several methodological features shaped the insights gained. The staged environments made it possible to observe how comfort evolved as participants moved from passive observation to active adjustment and then to a placebo-like condition involving fade-back. Embedding the study within a design-related task added ecological relevance by situating comfort within a realistic activity rather than isolated visual scenes. Because VR could only simulate visual and auditory adjustments, the study allowed a focused examination of perceptual and symbolic comfort cues in controlled conditions. Although exploratory, the combination of staged VR environments, task-based immersion, and the fade-back condition offered a structured approach for examining how comfort unfolds during different types of engagement and highlighted methodological pathways for future VR comfort research. This methodological structure supports understanding comfort as a dynamic experiential process rather than a single moment of evaluation. By tracing transitions between baseline exposure, the physical-control and lighting-control conditions, and a subsequent placebo-like condition, the study offers an exploratory framework for examining how comfort develops, is maintained, and changes across staged conditions in simulated occupied-building contexts.

Several limitations should be acknowledged. The modest sample size constrains generalizability, and the participant group was limited to students, which narrows the applicability of the findings to other user populations. In addition, the sample was drawn from two institutional settings, yet the study was not designed to examine cultural or climatic differences, limiting the relevance of the findings for cross-context comparison.

Methodological limitations also shaped the scope of the findings. The VR simulation could not reproduce genuine thermal or airflow changes; as a result, comfort was examined perceptually rather than physiologically. Participants also completed the experiment under naturally occurring classroom conditions that were not standardized, which may have influenced their interpretation of the VR cues. Additionally, the absence of physiological or behavioral measures limited examination of unconscious comfort responses, while the lack of task-performance metrics prevented analysis of how environmental adjustments may have affected cognitive effort or attention. Moreover, the design-related task was deliberately kept simple to minimize learning effects across the staged environments, which limits the applicability of the findings to more complex design activities typically performed in educational settings.

Additional limitations relate to interpretation of the later stages and the exploratory quantitative analysis. Variation in participants' familiarity with the real classroom may also have influenced perceptions of realism. Finally, the exploratory cross-tabulation analyses were based on a small sample and several low expected cell counts, limiting the robustness of the observed associations and requiring confirmation in larger samples.

Future research should include larger and more diverse samples across different institutions and learning environments to determine whether the patterns observed here are consistent across user groups. Comparative studies designed specifically to examine cultural and climatic differences could clarify whether these patterns remain consistent across contexts not examined in the present study. Further, integrating physiological or behavioral measures could provide insight into unconscious comfort processes, while including task-performance metrics would allow examination of how environmental adjustments influence cognitive effort, attention, or productivity.

Future work could also investigate how symbolic VR-based cues operate in relation to measured indoor environmental conditions through hybrid study designs that combine immersive simulation with monitored thermal, acoustic, and lighting variables. Such approaches may help clarify how perceptual interpretations of comfort interact with physical environmental conditions in adaptive learning environments. In addition, studies using more complex design-related tasks could examine whether the patterns observed here remain relevant under forms of engagement that more closely resemble studio-based educational activity.

Longer-term studies may also help determine how VR-based comfort cues function over repeated or extended use, particularly in dynamic classroom contexts where environmental demands fluctuate. Further research is also needed to examine whether and how such cues might support perceived comfort in buildings where opportunities for physical environmental control are limited, and to assess the longer-term feasibility of integrating these approaches into everyday learning environments.

This exploratory study examined comfort during a design-related task in a simulated occupied-building context using a VR-based replica of a real classroom. By comparing a baseline condition with staged opportunities for physical-control and lighting-control adjustments, followed by a placebo-like condition involving fade-back, the study showed how comfort developed across different levels of engagement and awareness. The findings point to perceived control as an important factor in shaping comfort during immersive task performance and suggest that symbolic VR-based cues may help support comfort where physical adjustment is limited. While exploratory, the study provides a foundation for further research on how perceived control, symbolic cues, and fade-back logic may inform potential comfort-support strategies in operational classrooms.

This study was approved by the KU Leuven Privacy and Ethics Platform (PRET) and the Social and Societal Ethics Committee (SMEC) under approval number G-2025–9551-R3(MIN). All participants provided written informed consent before participating. The research adhered to GDPR data protection requirements, and all identifying information was removed from study records.

The authors gratefully acknowledge RE:Lab (Italy) for their essential contribution in developing the virtual reality application and providing technical support. AI-assisted tools (ChatGPT by OpenAI) were used solely to improve language clarity and coherence. The authors assume full responsibility for the accuracy and interpretation of all results and conclusions presented in this paper.